School of Mechanical Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing, 210094, China.
State Key Laboratory of Organic Electronics and Information Displays & Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing, 210023, China.
Adv Sci (Weinh). 2024 Jun;11(23):e2400492. doi: 10.1002/advs.202400492. Epub 2024 Apr 3.
The cooperative diagnosis of non-coding RNAs (ncRNAs) can accurately reflect the state of cell differentiation and classification, laying the foundation of precision medicine. However, there are still challenges in simultaneous analyses of multiple ncRNAs and the integration of biomarker data for cell typing. In this study, DNA framework-based programmable atom-like nanoparticles (PANs) are designed to develop molecular classifiers for intra-cellular imaging of multiple ncRNAs associated with cell differentiation. The PANs-based molecular classifier facilitates signal amplification through the catalytic hairpin assembly. The interaction between PAN reporters and ncRNAs enables high-fidelity conversion of ncRNAs expression level into binding events, and the assessment of in situ ncRNAs levels via measurement of the fluorescent signal changes of PAN reporters. Compared to non-amplified methods, the detection limits of PANs are reduced by four orders of magnitude. Using human gastric cancer cell lines as a model system, the PANs-based molecular classifier demonstrates its capacity to measure multiple ncRNAs in living cells and assesses the degree of cell differentiation. This approach can serve as a universal strategy for the classification of cancer cells during malignant transformation and tumor progression.
非编码 RNA（ncRNAs）的合作诊断可以准确反映细胞分化和分类的状态，为精准医学奠定基础。然而，在同时分析多种 ncRNAs 以及整合细胞分型的生物标志物数据方面仍然存在挑战。在这项研究中，设计了基于 DNA 框架的可编程原子样纳米颗粒 (PANs)，以开发用于细胞内与细胞分化相关的多种 ncRNAs 成像的分子分类器。基于 PANs 的分子分类器通过催化发夹组装实现信号放大。PAN 报告器与 ncRNAs 之间的相互作用能够将 ncRNAs 表达水平的高保真度转化为结合事件，并通过测量 PAN 报告器的荧光信号变化来评估原位 ncRNAs 水平。与非放大方法相比，PANs 的检测限降低了四个数量级。用人胃癌细胞系作为模型系统，基于 PANs 的分子分类器证明了其在活细胞中测量多种 ncRNAs 并评估细胞分化程度的能力。这种方法可以作为恶性转化和肿瘤进展过程中癌细胞分类的通用策略。
